rail.estimation.algos.trainZ module
Implementation of the ‘pathological photo-z PDF estimator, as used in arXiv:2001.03621 (see section 3.3). It assigns each test set galaxy a photo-z PDF equal to the normalized redshift distribution N (z) of the training set.
- class rail.estimation.algos.trainZ.Inform_trainZ(args, comm=None)[source]
Bases:
CatInformerTrain an Estimator which returns a global PDF for all galaxies
- config_options = {'hdf5_groupname': <class 'str'>, 'nzbins': {'bands': ['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst'], 'dz': 0.01, 'err_bands': ['mag_err_u_lsst', 'mag_err_g_lsst', 'mag_err_r_lsst', 'mag_err_i_lsst', 'mag_err_z_lsst', 'mag_err_y_lsst'], 'hdf5_groupname': 'photometry', 'mag_limits': {'mag_g_lsst': 29.04, 'mag_i_lsst': 28.62, 'mag_r_lsst': 29.06, 'mag_u_lsst': 27.79, 'mag_y_lsst': 27.05, 'mag_z_lsst': 27.98}, 'nondetect_val': 99.0, 'nzbins': 301, 'redshift_col': 'redshift', 'ref_band': 'mag_i_lsst', 'zmax': 3.0, 'zmin': 0.0}, 'output_mode': <ceci.config.StageParameter object>, 'redshift_col': {'bands': ['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst'], 'dz': 0.01, 'err_bands': ['mag_err_u_lsst', 'mag_err_g_lsst', 'mag_err_r_lsst', 'mag_err_i_lsst', 'mag_err_z_lsst', 'mag_err_y_lsst'], 'hdf5_groupname': 'photometry', 'mag_limits': {'mag_g_lsst': 29.04, 'mag_i_lsst': 28.62, 'mag_r_lsst': 29.06, 'mag_u_lsst': 27.79, 'mag_y_lsst': 27.05, 'mag_z_lsst': 27.98}, 'nondetect_val': 99.0, 'nzbins': 301, 'redshift_col': 'redshift', 'ref_band': 'mag_i_lsst', 'zmax': 3.0, 'zmin': 0.0}, 'save_train': True, 'zmax': {'bands': ['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst'], 'dz': 0.01, 'err_bands': ['mag_err_u_lsst', 'mag_err_g_lsst', 'mag_err_r_lsst', 'mag_err_i_lsst', 'mag_err_z_lsst', 'mag_err_y_lsst'], 'hdf5_groupname': 'photometry', 'mag_limits': {'mag_g_lsst': 29.04, 'mag_i_lsst': 28.62, 'mag_r_lsst': 29.06, 'mag_u_lsst': 27.79, 'mag_y_lsst': 27.05, 'mag_z_lsst': 27.98}, 'nondetect_val': 99.0, 'nzbins': 301, 'redshift_col': 'redshift', 'ref_band': 'mag_i_lsst', 'zmax': 3.0, 'zmin': 0.0}, 'zmin': {'bands': ['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst'], 'dz': 0.01, 'err_bands': ['mag_err_u_lsst', 'mag_err_g_lsst', 'mag_err_r_lsst', 'mag_err_i_lsst', 'mag_err_z_lsst', 'mag_err_y_lsst'], 'hdf5_groupname': 'photometry', 'mag_limits': {'mag_g_lsst': 29.04, 'mag_i_lsst': 28.62, 'mag_r_lsst': 29.06, 'mag_u_lsst': 27.79, 'mag_y_lsst': 27.05, 'mag_z_lsst': 27.98}, 'nondetect_val': 99.0, 'nzbins': 301, 'redshift_col': 'redshift', 'ref_band': 'mag_i_lsst', 'zmax': 3.0, 'zmin': 0.0}}
- name = 'Inform_trainZ'
- class rail.estimation.algos.trainZ.TrainZ(args, comm=None)[source]
Bases:
CatEstimatorCatEstimator which returns a global PDF for all galaxies
- config_options = {'chunk_size': 10000, 'hdf5_groupname': <class 'str'>, 'nzbins': {'bands': ['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst'], 'dz': 0.01, 'err_bands': ['mag_err_u_lsst', 'mag_err_g_lsst', 'mag_err_r_lsst', 'mag_err_i_lsst', 'mag_err_z_lsst', 'mag_err_y_lsst'], 'hdf5_groupname': 'photometry', 'mag_limits': {'mag_g_lsst': 29.04, 'mag_i_lsst': 28.62, 'mag_r_lsst': 29.06, 'mag_u_lsst': 27.79, 'mag_y_lsst': 27.05, 'mag_z_lsst': 27.98}, 'nondetect_val': 99.0, 'nzbins': 301, 'redshift_col': 'redshift', 'ref_band': 'mag_i_lsst', 'zmax': 3.0, 'zmin': 0.0}, 'output_mode': <ceci.config.StageParameter object>, 'zmax': {'bands': ['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst'], 'dz': 0.01, 'err_bands': ['mag_err_u_lsst', 'mag_err_g_lsst', 'mag_err_r_lsst', 'mag_err_i_lsst', 'mag_err_z_lsst', 'mag_err_y_lsst'], 'hdf5_groupname': 'photometry', 'mag_limits': {'mag_g_lsst': 29.04, 'mag_i_lsst': 28.62, 'mag_r_lsst': 29.06, 'mag_u_lsst': 27.79, 'mag_y_lsst': 27.05, 'mag_z_lsst': 27.98}, 'nondetect_val': 99.0, 'nzbins': 301, 'redshift_col': 'redshift', 'ref_band': 'mag_i_lsst', 'zmax': 3.0, 'zmin': 0.0}, 'zmin': {'bands': ['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst'], 'dz': 0.01, 'err_bands': ['mag_err_u_lsst', 'mag_err_g_lsst', 'mag_err_r_lsst', 'mag_err_i_lsst', 'mag_err_z_lsst', 'mag_err_y_lsst'], 'hdf5_groupname': 'photometry', 'mag_limits': {'mag_g_lsst': 29.04, 'mag_i_lsst': 28.62, 'mag_r_lsst': 29.06, 'mag_u_lsst': 27.79, 'mag_y_lsst': 27.05, 'mag_z_lsst': 27.98}, 'nondetect_val': 99.0, 'nzbins': 301, 'redshift_col': 'redshift', 'ref_band': 'mag_i_lsst', 'zmax': 3.0, 'zmin': 0.0}}
- name = 'TrainZ'
- open_model(**kwargs)[source]
Load the mode and/or attach it to this Estimator
- Parameters:
model (object, str or ModelHandle) – Either an object with a trained model, a path pointing to a file that can be read to obtain the trained model, or a ModelHandle providing access to the trained model.
- Returns:
self.model – The object encapsulating the trained model.
- Return type:
object